Research Papers & Resources
5 articles
zkML Privacy-Preserving AI Training on Sensitive Data Without Raw Access
In an era where privacy-preserving machine learning is no longer optional but essential, particularly for sectors handling sensitive financial and health data, zkML emerges as a conservative yet transformative approach. Traditional AI...
Mar 26, 2026
Selective zkML Proofs: Verifying High-Risk AI Model Slices for Privacy-Preserving Inference
In the high-stakes world of financial modeling and healthcare diagnostics, AI inferences carry immense responsibility. A single erroneous output from a black-box model could trigger misguided investment decisions or misdiagnoses, yet full...
Feb 13, 2026
zkML for Privacy-Preserving LLM Fine-Tuning: Zero-Knowledge Proofs in Federated Pipelines
In the rush to harness large language models for specialized tasks, organizations grapple with a stark reality: fine-tuning these behemoths demands vast troves of sensitive data, often exposing trade secrets, patient records, or...
Feb 12, 2026
zkML Consensus Mechanisms Multi-Model Evaluation Mira Warden Protocol
In the evolving landscape of decentralized AI, zkML consensus mechanisms stand out as a prudent safeguard against the inherent uncertainties of machine learning models. By marrying zero-knowledge proofs with multi-model evaluation...
Feb 4, 2026
Scaling zkML Proofs for Large Models Inference Labs Sharding Approach
In the evolving landscape of artificial intelligence, where models balloon to hundreds of billions of parameters, verifying computations without exposing sensitive data emerges as a cornerstone challenge. Inference Labs addresses this...
Feb 4, 2026